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Emotion recognition, facilitated by artificial intelligence (AI) technologies, has emerged as a crucial area of research with implications spanning human-computer interaction, healthcare, and beyond. This paper presents a comprehensive systematic review spanning the years 2014 to 2023, analyzing the progress, methodologies, and implications of emotion recognition in conjunction with AI. The review begins by examining the evolution of emotion recognition techniques, focusing on the transition from traditional methods to deep learning-based approaches. It synthesizes findings from a wide range of studies, elucidating the strengths and limitations of various AI models in facial emotion detection, speech analysis, and multimodal emotion recognition. Furthermore, the paper explores the applications of emotion recognition technologies across diverse domains, including affective computing, mental health monitoring, and personalized user interfaces. It highlights the potential societal impacts of these technologies while addressing ethical considerations such as privacy, bias, and algorithmic transparency. Moreover, the review identifies key research gaps and challenges within the field, including issues related to dataset bias, cross-cultural variations in emotional expression, and the need for real-time processing capabilities. Drawing upon the insights gleaned from the systematic review, the paper offers research recommendations to address these challenges and advance the state-of-the-art in emotion recognition. In particular, the paper proposes leveraging deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for more accurate and robust facial emotion detection. It also advocates for interdisciplinary collaboration and the integration of multimodal data sources to enhance the richness and nuance of emotion recognition systems. In conclusion, this paper provides a comprehensive overview of the advancements and challenges in emotion recognition with AI, spanning the past decade. By delineating future research directions and recommendations, it aims to catalyze further innovation in this rapidly evolving field, with the potential to revolutionize human-computer interaction and societal well-being.

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Enhancing Accuracy and Efficiency Analysis for Emotion Detection and Prediction Using Deep Learning Model

  • 会议论文

  • 发表日期：

    2025年

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  • Ravi Ray Chaudhari,
  • Himanshi Agrawal

摘要

Emotion recognition, facilitated by artificial intelligence (AI) technologies, has emerged as a crucial area of research with implications spanning human-computer interaction, healthcare, and beyond. This paper presents a comprehensive systematic review spanning the years 2014 to 2023, analyzing the progress, methodologies, and implications of emotion recognition in conjunction with AI. The review begins by examining the evolution of emotion recognition techniques, focusing on the transition from traditional methods to deep learning-based approaches. It synthesizes findings from a wide range of studies, elucidating the strengths and limitations of various AI models in facial emotion detection, speech analysis, and multimodal emotion recognition. Furthermore, the paper explores the applications of emotion recognition technologies across diverse domains, including affective computing, mental health monitoring, and personalized user interfaces. It highlights the potential societal impacts of these technologies while addressing ethical considerations such as privacy, bias, and algorithmic transparency. Moreover, the review identifies key research gaps and challenges within the field, including issues related to dataset bias, cross-cultural variations in emotional expression, and the need for real-time processing capabilities. Drawing upon the insights gleaned from the systematic review, the paper offers research recommendations to address these challenges and advance the state-of-the-art in emotion recognition. In particular, the paper proposes leveraging deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for more accurate and robust facial emotion detection. It also advocates for interdisciplinary collaboration and the integration of multimodal data sources to enhance the richness and nuance of emotion recognition systems. In conclusion, this paper provides a comprehensive overview of the advancements and challenges in emotion recognition with AI, spanning the past decade. By delineating future research directions and recommendations, it aims to catalyze further innovation in this rapidly evolving field, with the potential to revolutionize human-computer interaction and societal well-being.